Relatively frequent color calibration of a color print device is highly desirable since the colors actually printed on the output media (as compared to the colors intended to be printed) can significantly change, or drift, out of calibration over time. For example, changes in the selected or loaded print media, such as differences paper or plastic sheet types, materials, weights, calendaring, coating, humidity, etc., or changes in the printer's ambient conditions, changes in the image developer materials, aging or wear of printer components, varying interactions of different colors, etc., can all affect output color reproduction quality. One method for assuring color quality is to accurately measure the colors being reproduced by the color print device and calibrate the device accordingly. Spectroscopy is used in this regard.
Spectroscopy is the measurement and analysis of electromagnetic radiation absorbed, scattered, or emitted by atoms, molecules, or other chemical or physical materials. When light waves strike a surface, some of the spectrum's energy is absorbed by the surface while other parts of the spectrum are reflected. The light that is reflected has its own composition of various wavelengths. Different surfaces containing pigments, dyes, inks, and the like, which generate different but unique wavelength compositions. Light can be modified by striking a reflective surface such as paper, or by passing through a transmissive object such as film. The pattern of wavelengths that reflects from the object's surface is the object's spectral data.
Multi-illuminator spectrophotometric reflectance sensing systems, especially those suitable for high speed inline document color analysis, must be calibrated and characterized in accordance with particular operating characteristics of known illumination sources and reflectance sensors. Accurate device calibration depends on the accuracies of the illuminators and sensors. Sensor-to-sensor variations are largely due to differences in light emission curves, peak wavelengths, and full width half max values in the emission spectra. Moreover, when a sensor is installed on a print device, it may require personalization to improve accuracy due to variations in mounting, ambient and mechanical tolerances. Such differences can give rise to large variations in measurement accuracy. Correcting for often subtle variations between sensors requires that a characterization be performed on a per-sensor basis. This is usually performed during manufacturing. Such a characterization can be time consuming and labor intensive, which can contribute to the overall cost of manufacturing the sensing instrument. Moreover, sensor-to-sensor variations dictate that the database of training sample measurements taken using a first sensor cannot be used to calibrate a second sensor because slight differences in peak wavelengths across illuminators can produce a dramatic difference in the measurements taken by the two sensing devices; particularly if the spectral reflectance curve of the training sample has a steep slope in that area of the visible light range. As such, a reconstruction matrix cannot be generated for the second sensor using the first sensor's training sample measurements. This is how sensors have traditionally been characterizing. What has not been achieved is generating reconstruction matrices for subsequent sensors using training sample measurements from a single sensor.
Accordingly, what is needed in this art are systems and methods for quickly characterizing a model-based spectral reflectance sensing device without measuring the full set of characterization color patch training samples currently used in manufacturing and characterizing individual reflectance sensing devices.